Impact of SST Nudging on CFSR Analysis and Seasonal ENSO Forecasts

Ocean reanalysis (ORA) systems often constrain their sea surface temperature (SST) by strongly nudging the top ocean model layer toward external SST analyses. Different SST analyses may be adopted in operational ORA due to practical configuration limitations. However, impacts of switching SST datasets for nudging on ORAs and forecasts have rarely been examined. In February 2020, the Climate Forecast System Reanalysis (CFSR) switched from the long-used NOAA OISSTv2 dataset to NCEP’s Near-Surface Sea Temperature (NSST). This study investigates how this change influenced CFSR SST and two seasonal forecast systems that rely on it for ocean initial conditions: CFSv2 and CCSM4. The switch introduced a sharp discontinuity in CFSR SST, with negligible differences relative to OISSTv2.1 before 2020 but a large bias afterward. Errors exceeded − 1°C along western boundary currents and the Antarctic Circumpolar Current and about − 0.2 − 0.5°C in tropical upwelling zones. These biases propagated almost undamped into forecasts initialized from CFSR. At 0-month forecast lead, both CFSv2 and CCSM4 exhibited cold anomalies in the eastern tropical Pacific, which intensified and spread westward toward the dateline within four months. ENSO composites show that forecasts after 2020 had systematically cold biases during boreal fall and winter, impacting the strength of ENSO. This cooling trend reversed an earlier warm bias associated with a known CFSR discontinuity around 1999 and led to overly cold La Niña forecasts in 2024, even during high-skill ENSO seasons. These findings highlight that changing the SST dataset for ORA can introduce nonstationary errors affecting real-time forecasts.